Utilizing storage unit latency data in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) processing unit includes generating a first access request for transmission via a network to a first one of a plurality of storage units in a dispersed storage network (DSN). A first access response is received via the network from the first one of the plurality of storage units that includes a first access time duration. Access duration data is updated to include the first access time duration received from the first one of the plurality of storage units. A subset of storage units is selected from the plurality of storage units based on comparing a plurality of access time durations corresponding to the plurality of storage units included in the access duration data to perform a second data access. At least one second access request is generated for transmission via the network to the subset of storage units.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/248,752,entitled “MIGRATING DATA IN A DISPERSED STORAGE NETWORK”, filed Oct. 30,2015, which is hereby incorporated herein by reference in its entiretyand made part of the present U.S. Utility patent application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 10 is a logic diagram of an example of a method of utilizingstorage unit latency data in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the 10 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a computing device 16 of FIG. 1, thenetwork 24 of FIG. 1, and a plurality of storage units 1-n. Thecomputing device 16 can include the interface 32 of FIG. 1, thecomputing core 26 of FIG. 1, and the DS client module 34 of FIG. 1. Thecomputing device 16 can function as a dispersed storage processing agentfor computing device 14 as described previously, and may hereafter beinterchangeably referred to as a distributed storage and task (DST)processing unit. Each storage unit may be implemented utilizing thestorage unit 36 of FIG. 1, and can each function as a dispersed storageand task (DST) execution unit as described previously. The DSN functionsto utilize storage unit latency data when executing access requests.

In various embodiments, a DST processing unit can make intelligentdecisions when executing data access requests such as write requests,read requests, and/or task execution requests based on knowledge ofwhich storage units are operating the best and fastest vs. which onesare operating the most slowly or are most burdened. The DST processingunit can take into account the total amount of time it took from whenthe DST processing unit issued the request and when the DST processingunit received a response. However, in various embodiments, thismechanism can conflate properties of the network such as latency,congestion, packet loss, etc., with actual behavior of one or morestorage units executing the request.

In various embodiments, these two variables can be distinguished. Uponreceiving a request from a DST processing unit via the network, thestorage unit can record the time the request was received forprocessing. Then, after processing the request and assembling aresponse, the storage unit can record the time the response wasgenerated. The storage unit can send both of these recorded times backas fields within the response (or alternatively, can send the differencebetween these times). The response can then be sent back to the DSTprocessing unit that issued the request. The DST processing unit canthereby compare the actual processing time within each storage unit toother processing times of accesses. For example, the processing time ofa storage unit can be compared to previous access times of the samestorage unit or different units. The processing times of a plurality ofstorage units executing related requests at that time can also becompared, for example, a plurality of processing times corresponding toread and/or write requests for slices of a particular data objectdispersed amongst the plurality of storage units.

The DST processing unit can store received access durations as accessduration data, for example, in a log of access durations. The accessduration data of past data access can be used when the DST processingunit is selecting a subset of storage units to utilize when executing acurrent access request. For example, a plurality of n write requestscorresponding to n encoded slices of a data object can be sent to asubset of n storage units with fast previous access times, for example,the n storage units with the fastest access times. In variousembodiments the number of storage units n may be based on an informationdispersal algorithm (IDA) parameter such as a read threshold, writethreshold, or column width.

In various embodiments, the type of access considered in determiningwhich storage units will be utilized based on types of accessescorresponding the previous access durations in the access duration data.For example, the DST processing unit can choose to consider only accessduration times corresponding to access types that match the currentaccess type. In various embodiments, timestamps can be included in theaccess duration data, and the DST processing unit can further selectstorage units based on the timestamps. For example, the DST processingunit can choose to consider only access duration times corresponding torecent accesses of storage units. In various embodiments, the DSTprocessing unit can periodically update the access duration data byremoving access duration times that are older than a certain threshold,or to store only a subset of recent access duration times for eachstorage unit.

In various embodiments, a DST processing unit can utilize uniquecombination reads (UCR) when encoding a data object as a plurality ofencoded data slices distributed amongst a plurality of storage units,and utilize the access duration data of the storage units whenrecovering the data object. The number of data slices needed to decodeand regenerate the original data object is less than the total number ofdata slices stored for the object. In various embodiments, k slices arenecessary to decode the object, and there are n slices total. In variousembodiments, any combination of k slices can be used jointly to decodethe object. Each combination of k slices for a data object can beassigned to a particular requesting entity, where the requesting entitycan be, for example, a user device 12-14. In such cases, the combinationof k slices assigned to the communicating for the data object can be aunique combination of read requests for the object if the total numberof requesting entities does not exceed C(n,k), the total number ofpossible combinations of k slices from the total number of slices n. Invarious embodiments, the number of possible read combinations k cancorrespond to the number of users. In such embodiments where UCR isutilized, the DST processing unit can select the particular combinationof k slices to be read based on previous access duration timescorresponding to the n storage units. For example, the combination of kslices stored in the storage units with the fastest past accessdurations can be selected to be read.

In various embodiments, the access duration data can be used to detectstorage units that are particularly slow, perhaps due to a problem thatneeds to be addressed. The DST processing unit can evaluate the accessduration data to identify particularly slow storage units, and cangenerate a notification for transmission via the network, for example,to a user of the network. This information can be used by an entity thatcan then run diagnostics, repair, and/or replace one or more slowstorage units. Slow storage units can be identified based on accessduration times below a threshold, access duration times that arecompared to a variation or standard deviation and declared statisticallysignificant, etc.

In various embodiments, the received access duration times from thestorage units included in access responses can aid the DST processingunit in determining the how much of the total time needed to process arequest is due to network latency, and how much is due to latency withinthe storage unit itself. The DST processing unit, when provided startand end times from the DST unit, as discussed, can also compare latencyof the network in sending the request. The DST processing unit can alsocompute latency of the network in sending the response, for example,when the DST processing unit and storage unit's clocks are synchronized.The DST processing unit can determine when network latency is lessimportant than burden placed on the storage unit, such as when selectingstorage units to perform rebuild work.

Furthermore, the DST processing unit can determine if the networklatency overpowers the storage unit processing time, and can foregoconsidering storage unit access durations if they are not significantwith respect to the total access time. Similarly, the DST processingunit can determine if a variation amongst storage unit access durationtimes is significant with respect to the total access time, for example,by comparing a difference between slowest and fastest storage unitaccess times to the total access time, comparing a standard deviation ofstorage unit access times to the total access time, etc. The DSTprocessing unit can determine a network latency significance score bycomparing one or more access time durations included in the accessduration data to the network latency, and if a network latencysignificance score compares unfavorably to a significance scorethreshold, indicating that that the network latency is less significantthan the at least one of the plurality of access time durations, thenthe subset of storage units to be used for the access is selected basedon of the plurality of access time durations indicated in the accessduration data as discussed. In various embodiments, when the networklatency significance score instead compares favorably to thesignificance score threshold, indicating that the network latency ismore significant than the at least one of the plurality of access timedurations, the subset of storage units can be selected independently ofthe plurality of access time durations. In various embodiments, thenetwork latency is computed based on subtracting an average storage unitaccess duration or particular storage unit access duration from a totalaccess duration determined by the DST processing unit based on accessrequest transmission times and access response retrieval times. Invarious embodiments, a particular storage unit access duration used tocompute network latency is computed based on a corresponding access typeand/or a corresponding access timestamp.

In various embodiments, a processing system of a dispersed storage andtask (DST) processing unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to generate a first accessrequest for transmission via a network to a first one of a plurality ofstorage units in a dispersed storage network (DSN). A first accessresponse is received via the network from the first one of the pluralityof storage units that includes a first access time duration. Accessduration data is updated to include the first access time durationreceived from the first one of the plurality of storage units. A subsetof storage units is selected from the plurality of storage units basedon comparing a plurality of access time durations corresponding to theplurality of storage units included in the access duration data toperform a second data access. At least one second access request isgenerated for transmission via the network to the subset of storageunits.

In various embodiments, the first access time duration is based on adifference between a first time that the first access request wasreceived by the first one of the plurality of storage units and a secondtime that the first access response was generated by the first one ofthe plurality of storage units. In various embodiments, the subset ofstorage units is selected based on ranking the access time durations andincluding storage units with fastest ranked corresponding access timedurations. In various embodiments, the size of the subset of storageunits is based on an information dispersal algorithm (IDA) parameter. Invarious embodiments, the second data access corresponds to a readrequest, and the subset of storage units is further selected based on aunique combination reads (UCR) protocol.

In various embodiments, the access duration data further includes aplurality of access types corresponding to the plurality of access timedurations. A subset of access time durations is selected from theplurality of access time durations by including access time durationsthat each correspond to a one of the plurality of access types thatmatches a data access type corresponding to the second data access. Thesubset of storage units is selected based on comparing only access timedurations included in the subset of access time durations. In variousembodiments, the access duration data includes a plurality of timestampscorresponding to the access time durations, and selecting the subset ofstorage units is further based on comparing the plurality of timestamps.In various embodiments, at least one of the plurality of storage unitshas a plurality of corresponding access time durations included in theaccess duration data. A subset of access time durations is selected fromthe plurality of access time durations by including a most recent of theplurality of corresponding access time durations for each of the atleast one of the plurality of storage units. The subset of storage unitsis selected based on comparing only access time durations included inthe subset of access time durations.

In various embodiments, latency data is generated that includes anetwork latency significance score by comparing at least one of theplurality of access time durations included in the access duration datato a network latency. The subset of storage units is selected based onthe plurality of access time durations when the network latencysignificance score compares unfavorably to a threshold significancescore, indicating that the network latency is less significant than theat least one of the plurality of access time durations. The subset ofstorage units is selected independently of the plurality of access timedurations when the network latency significance score compares favorablyto a threshold significance score, indicating that the network latencyis more significant than the at least one of the plurality of accesstime durations. In various embodiments, the network latency significancescore indicates that the network latency is more significant than the atleast one of the plurality of access time durations when the networklatency is greater than the at least one of the plurality of access timedurations by at least a fixed significance factor. The network latencysignificance score indicates that the network latency is lesssignificant than the at least one of the plurality of access timedurations when the network latency is not greater than the at least oneof the plurality of access time durations by at least the fixedsignificance factor. In various embodiments, the network latency iscalculated by subtracting one of the plurality of access time durationsthat corresponds to one of a plurality of past accesses from a totaltime. The total time is determined by a difference between a first timethat a past access request corresponding to the one of the plurality ofpast accesses was transmitted by the DST processing unit and a secondtime that a past access response corresponding to the one of theplurality of past accesses was received by the DST processing unit. Invarious embodiments, the one of the plurality of access time durationsis selected to calculate the network latency based on a correspondingaccess type, a corresponding access timestamp, and/or an average accesstime duration associated with the plurality of access time durations.

In various embodiments, a slow storage unit subset of the plurality ofstorage units is selected by ranking the plurality of access timedurations and including storage units with slowest ranked correspondingaccess time durations. A notification is generated for transmission viathe network that indicates the slow storage unit subset. In variousembodiments, selecting the slow storage unit subset includes selectingstorage units with corresponding access times that are slower than afixed access duration threshold.

FIG. 10 is a flowchart illustrating an example of functions to utilizestorage unit latency data when executing access requests. In particular,a method is presented for use in association with one or more functionsand features described in conjunction with FIGS. 1-9, for execution by adispersed storage and task (DST) processing unit that includes aprocessor or via another processing system of a dispersed storagenetwork that includes at least one processor and memory that storesinstruction that configure the processor or processors to perform thesteps described below. Step 1002 includes generating a first accessrequest for transmission via a network to a first one of a plurality ofstorage units in a dispersed storage network (DSN). Step 1004 includesreceiving a first access response via the network from the first one ofthe plurality of storage units that includes a first access timeduration. Step 1006 includes updating access duration data to includethe first access time duration received from the first one of theplurality of storage units. Step 1008 includes selecting a subset ofstorage units from the plurality of storage units based on comparing aplurality of access time durations corresponding to the plurality ofstorage units included in the access duration data to perform a seconddata access. Step 1010 includes generating at least one second accessrequest for transmission via the network to the subset of storage units.

In various embodiments, the first access time duration is based on adifference between a first time that the first access request wasreceived by the first one of the plurality of storage units and a secondtime that the first access response was generated by the first one ofthe plurality of storage units. In various embodiments, the subset ofstorage units is selected based on ranking the access time durations andincluding storage units with fastest ranked corresponding access timedurations. In various embodiments, the size of the subset of storageunits is based on an information dispersal algorithm (IDA) parameter. Invarious embodiments, the second data access corresponds to a readrequest, and the subset of storage units is further selected based on aunique combination reads (UCR) protocol.

In various embodiments, the access duration data further includes aplurality of access types corresponding to the plurality of access timedurations. A subset of access time durations is selected from theplurality of access time durations by including access time durationsthat each correspond to a one of the plurality of access types thatmatches a data access type corresponding to the second data access. Thesubset of storage units is selected based on comparing only access timedurations included in the subset of access time durations. In variousembodiments, the access duration data includes a plurality of timestampscorresponding to the access time durations, and selecting the subset ofstorage units is further based on comparing the plurality of timestamps.In various embodiments, at least one of the plurality of storage unitshas a plurality of corresponding access time durations included in theaccess duration data. A subset of access time durations is selected fromthe plurality of access time durations by including a most recent of theplurality of corresponding access time durations for each of the atleast one of the plurality of storage units. The subset of storage unitsis selected based on comparing only access time durations included inthe subset of access time durations.

In various embodiments, latency data is generated that includes anetwork latency significance score by comparing at least one of theplurality of access time durations included in the access duration datato a network latency. The subset of storage units is selected based onthe plurality of access time durations when the network latencysignificance score compares unfavorably to a threshold significancescore, indicating that the network latency is less significant than theat least one of the plurality of access time durations. The subset ofstorage units is selected independently of the plurality of access timedurations when the network latency significance score compares favorablyto a threshold significance score, indicating that the network latencyis more significant than the at least one of the plurality of accesstime durations. In various embodiments, the network latency significancescore indicates that the network latency is more significant than the atleast one of the plurality of access time durations when the networklatency is greater than the at least one of the plurality of access timedurations by at least a fixed significance factor. The network latencysignificance score indicates that the network latency is lesssignificant than the at least one of the plurality of access timedurations when the network latency is not greater than the at least oneof the plurality of access time durations by at least the fixedsignificance factor. In various embodiments, the network latency iscalculated by subtracting one of the plurality of access time durationsthat corresponds to one of a plurality of past accesses from a totaltime. The total time is determined by a difference between a first timethat a past access request corresponding to the one of the plurality ofpast accesses was transmitted by the DST processing unit and a secondtime that a past access response corresponding to the one of theplurality of past accesses was received by the DST processing unit. Invarious embodiments, the one of the plurality of access time durationsis selected to calculate the network latency based on a correspondingaccess type, a corresponding access timestamp, and/or an average accesstime duration associated with the plurality of access time durations.

In various embodiments, a slow storage unit subset of the plurality ofstorage units is selected by ranking the plurality of access timedurations and including storage units with slowest ranked correspondingaccess time durations. A notification is generated for transmission viathe network that indicates the slow storage unit subset. In variousembodiments, selecting the slow storage unit subset includes selectingstorage units with corresponding access times that are slower than afixed access duration threshold.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to generate a first access request for transmissionvia a network to a first one of a plurality of storage units in adispersed storage network (DSN). A first access response is received viathe network from the first one of the plurality of storage units thatincludes a first access time duration. Access duration data is updatedto include the first access time duration received from the first one ofthe plurality of storage units. A subset of storage units is selectedfrom the plurality of storage units based on comparing a plurality ofaccess time durations corresponding to the plurality of storage unitsincluded in the access duration data to perform a second data access. Atleast one second access request is generated for transmission via thenetwork to the subset of storage units.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a dispersed storage andtask (DST) processing unit that includes a processor, the methodcomprises: generating a first access request for transmission via anetwork to a first one of a plurality of storage units in a dispersedstorage network (DSN); receiving a first access response via the networkfrom the first one of the plurality of storage units that includes afirst access time duration; updating access duration data to include thefirst access time duration received from the first one of the pluralityof storage units; selecting a subset of storage units from the pluralityof storage units based on comparing a plurality of access time durationscorresponding to the plurality of storage units included in the accessduration data to perform a second data access; and generating at leastone second access request for transmission via the network to the subsetof storage units.
 2. The method of claim 1, wherein the first accesstime duration is based on a difference between a first time that thefirst access request was received by the first one of the plurality ofstorage units and a second time that the first access response wasgenerated by the first one of the plurality of storage units.
 3. Themethod of claim 1, wherein the subset of storage units is selected basedon ranking the access time durations and including storage units withfastest ranked corresponding access time durations.
 4. The method ofclaim 1, wherein the size of the subset of storage units is based on aninformation dispersal algorithm (IDA) parameter.
 5. The method of claim1, wherein the access duration data further includes a plurality ofaccess types corresponding to the plurality of access time durations,further comprising: selecting a subset of access time durations from theplurality of access time durations by including access time durationsthat each correspond to a one of the plurality of access types thatmatches a data access type corresponding to the second data access;wherein the subset of storage units is selected based on comparing onlyaccess time durations included in the subset of access time durations.6. The method of claim 1, wherein the access duration data includes aplurality of timestamps corresponding to the access time durations, andwherein selecting the subset of storage units is further based oncomparing the plurality of timestamps.
 7. The method of claim 6, whereinat least one of the plurality of storage units has a plurality ofcorresponding access time durations included in the access durationdata, further comprising: selecting a subset of access time durationsfrom the plurality of access time durations by including a most recentof the plurality of corresponding access time durations for each of theat least one of the plurality of storage units; wherein the subset ofstorage units is selected based on comparing only access time durationsincluded in the subset of access time durations.
 8. The method of claim1, further comprising: generating latency data that includes a networklatency significance score by comparing at least one of the plurality ofaccess time durations included in the access duration data to a networklatency; wherein the subset of storage units is selected based on theplurality of access time durations when the network latency significancescore compares unfavorably to a threshold significance score, wherebythe network latency is less significant than the at least one of theplurality of access time durations; and wherein the subset of storageunits is selected independently of the plurality of access timedurations when the network latency significance score compares favorablyto a threshold significance score, whereby the network latency is moresignificant than the at least one of the plurality of access timedurations.
 9. The method of claim 8, wherein the network latencysignificance score indicates that the network latency is moresignificant than the at least one of the plurality of access timedurations when the network latency is greater than the at least one ofthe plurality of access time durations by at least a fixed significancefactor, and wherein the network latency significance score indicatesthat the network latency is less significant than the at least one ofthe plurality of access time durations when the network latency is notgreater than the at least one of the plurality of access time durationsby at least the fixed significance factor.
 10. The method of claim 8,further comprising: calculating the network latency by subtracting oneof the plurality of access time durations that corresponds to one of aplurality of past accesses from a total time; wherein the total time isdetermined by a difference between a first time that a past accessrequest corresponding to the one of the plurality of past accesses wastransmitted by the DST processing unit and a second time that a pastaccess response corresponding to the one of the plurality of pastaccesses was received by the DST processing unit.
 11. The method ofclaim 10, further comprising selecting the one of the plurality ofaccess time durations to calculate the network latency based on at leastone of: a corresponding access type, a corresponding access timestamp,or an average access time duration associated with the plurality ofaccess time durations.
 12. The method of claim 1, further comprising:selecting a slow storage unit subset of the plurality of storage unitsby ranking the plurality of access time durations and including storageunits with slowest ranked corresponding access time durations; andgenerating a notification for transmission via the network indicatingthe slow storage unit subset.
 13. The method of claim 12, whereinselecting the slow storage unit subset includes selecting storage unitswith corresponding access times that are slower than a fixed accessduration threshold.
 14. The method of claim 1, wherein the second dataaccess corresponds to a read request, and wherein the subset of storageunits is further selected based on a unique combination reads (UCR)protocol.
 15. A processing system of a dispersed storage and task (DST)processing unit comprises: at least one processor; a memory that storesoperational instructions, that when executed by the at least oneprocessor cause the processing system to: generate a first accessrequest for transmission via a network to a first one of a plurality ofstorage units in a dispersed storage network (DSN); receive a firstaccess response via the network from the first one of the plurality ofstorage units that includes a first access time duration; update accessduration data to include the first access time duration received fromthe first one of the plurality of storage units; select a subset ofstorage units from the plurality of storage units based on comparing aplurality of access time durations corresponding to the plurality ofstorage units included in the access duration data to perform a seconddata access; and generate at least one second access request fortransmission via the network to the subset of storage units.
 16. Theprocessing system of claim 15, wherein the first access time duration isbased on a difference between a first time that the first access requestwas received by the first one of the plurality of storage units and asecond time that the first access response was generated by the firstone of the plurality of storage units.
 17. The processing system ofclaim 15, wherein the access duration data further includes a pluralityof access types corresponding to the plurality of access time durations,and wherein the operation instructions, when executed by the at leastone processor, further cause the processing system to: select a subsetof access time durations from the plurality of access time durations byincluding access time durations that each correspond to a one of theplurality of access types that matches a data access type correspondingto the second data access; wherein the subset of storage units isselected based on comparing only access time durations included in thesubset of access time durations.
 18. The processing system of claim 15,wherein the operation instructions, when executed by the at least oneprocessor, further cause the processing system to: generate latency datathat includes a network latency significance score by comparing at leastone of the plurality of access time durations included in the accessduration data to a network latency; wherein the subset of storage unitsis selected based on the plurality of access time durations when thenetwork latency significance score compares unfavorably to a thresholdsignificance score, whereby the network latency is less significant thanthe at least one of the plurality of access time durations; and whereinthe subset of storage units is selected independently of the pluralityof access time durations when the network latency significance scorecompares favorably to a threshold significance score, whereby thenetwork latency is more significant than the at least one of theplurality of access time durations.
 19. The processing system of claim15, wherein the operation instructions, when executed by the at leastone processor, further cause the processing system to: select a slowstorage unit subset of the plurality of storage units by ranking theplurality of access time durations and including storage units withslowest ranked corresponding access time durations; and generate anotification for transmission via the network indicating the slowstorage unit subset.
 20. A non-transitory computer readable storagemedium comprises: at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to: generate a first access request for transmissionvia a network to a first one of a plurality of storage units in the DSN;receive a first access response via the network from the first one ofthe plurality of storage units that includes a first access timeduration; update access duration data to include the first access timeduration received from the first one of the plurality of storage units;select a subset of storage units from the plurality of storage unitsbased on comparing a plurality of access time durations corresponding tothe plurality of storage units included in the access duration data toperform a second data access; and generate at least one second accessrequest for transmission via the network to the subset of storage units.